Medical genetics involves diagnosis, management, and determination of risk of hereditary disorders. Understanding the genotype-phenotype correlation of gene variants in disease is a major component of medical genetics. In monogenic diseases, gene mutations are typically curated as either “pathogenic” or “benign.” However, many gene variants (i.e., gene mutations) are classified as being “unknown” or “uncertain” because they cannot be clearly associated with a clinical phenotype. Accurate interpretation of gene testing, including accurate phenotype association of gene variants, is an important component in customization of healthcare such that decisions and practices provided to a patient are tailored to the individual patient.
In the recent years, various efforts, such as the Human Variome Project, 1000 Genomes, and NCBI Genetic Testing Registry, have resulted in a growing interest in annotation and clinical interpretation of gene variants in human diseases. Further, with rapidly evolving technologies (e.g., Single Nucleotide Polymorphisms (SNP) chip genome wide association studies and next-generation sequencing), genomic analysis has become faster and more cost effective, yielding much larger data sets than previously available. However, there exists a gap between the rapidly growing collections of genetic variation (i.e., genetic mutation) and practical clinical implementation. Further, as genetic information is incorporated into the electronic medical record, new decision support approaches are needed to provide clinicians with a preferred course of treatment. Moreover, for decision support rules to add value, the clinical relevance of laboratory information should be well understood.
Gene variant classification is critical in informing clinicians of the most appropriate course of treatment. To that end, medical geneticists typically rely on patient history and family segregation, literature review and trusted colleagues to stay informed of the phenotype consequences of a given gene variant. Although computer-based prediction methods may be employed to classify gene variants, there still exists a lack of a widely accepted standard computational predictor of mutation severity for novel or uncertain gene variants in clinical use. Further, existing prediction methods, despite being actively used in laboratories, do not offer sufficient accuracy to predict disease phenotype to the degree necessary to be clinically applicable.
In the recent years, updated recommendations on reporting and classification of gene variants, including approaches targeted at determining the clinical significance of variants of uncertain significance, have been proposed from the American College of Medical Geneticists (ACMG). Further, in order to improve interpretation of unclassified genetic variants, definitions and terminology have also been recommended by the International Agency for Research on Cancer (IARC).
Despite these recommendations, terms such as “deleterious,” “mutation,” “pathogenic,” or “causative of disease” are still being used in reporting genetic tests. Further, test results such as “indeterminate,” “unknown,” “uncertain,” “unclassified,” or “undetermined” render interpretation of the significance of a gene test result difficult. Further compounding this issue, word modifiers such as “likely,” “suspected,” “predicted,” “mild,” “moderate,” or “severe” often are used to accompany variant classification.
The lack of a quantitative metric or a standardized scale for evaluation of novel or uncertain gene variants render test result interpretation difficult and subjective to location and expertise at hand. A second and closely related challenge is the lack of an objective and standardized framework or context to make that metric meaningful. The quantitative metric and framework for evaluation become especially critical for interpretation of novel and uncertain gene variants where there is the obvious lack of traditional or existing evidence such as family history, pedigree trios or sib pairs, confirming literature reports, bench assay biochemical evidence, or colleague consensus of disease association.